{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=2019\n",
    "month=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import weighted\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and monthly_salary<80000\", con=db.get_conn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87621, 94)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_original.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "        {\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_77d157dc_e58e_11e9_9929_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col5\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col6\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col7\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow0_col1\" class=\"data row0 col1\" >haskell</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow0_col2\" class=\"data row0 col2\" >21516</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow0_col3\" class=\"data row0 col3\" >19500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow0_col4\" class=\"data row0 col4\" >7500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow0_col5\" class=\"data row0 col5\" >45000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow0_col6\" class=\"data row0 col6\" >61</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow1_col1\" class=\"data row1 col1\" >rust</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow1_col2\" class=\"data row1 col2\" >19561</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow1_col3\" class=\"data row1 col3\" >17500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow1_col4\" class=\"data row1 col4\" >4697</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow1_col5\" class=\"data row1 col5\" >47326</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow1_col6\" class=\"data row1 col6\" >377</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow2_col1\" class=\"data row2 col1\" >lua</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow2_col2\" class=\"data row2 col2\" >17739</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow2_col3\" class=\"data row2 col3\" >15500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow2_col4\" class=\"data row2 col4\" >6000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow2_col5\" class=\"data row2 col5\" >35000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow2_col6\" class=\"data row2 col6\" >3229</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow2_col7\" class=\"data row2 col7\" >0.83%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow3_col1\" class=\"data row3 col1\" >matlab</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow3_col2\" class=\"data row3 col2\" >17690</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow3_col3\" class=\"data row3 col3\" >17500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow3_col4\" class=\"data row3 col4\" >5250</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow3_col5\" class=\"data row3 col5\" >40000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow3_col6\" class=\"data row3 col6\" >5719</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow3_col7\" class=\"data row3 col7\" >1.47%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow4_col2\" class=\"data row4 col2\" >17444</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow4_col4\" class=\"data row4 col4\" >4000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow4_col5\" class=\"data row4 col5\" >40000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow4_col6\" class=\"data row4 col6\" >29870</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow4_col7\" class=\"data row4 col7\" >7.68%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow5_col1\" class=\"data row5 col1\" >go</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow5_col2\" class=\"data row5 col2\" >17405</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow5_col3\" class=\"data row5 col3\" >15000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow5_col4\" class=\"data row5 col4\" >6000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow5_col5\" class=\"data row5 col5\" >37500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow5_col6\" class=\"data row5 col6\" >28187</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow5_col7\" class=\"data row5 col7\" >7.25%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow6_col1\" class=\"data row6 col1\" >perl</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow6_col2\" class=\"data row6 col2\" >16065</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow6_col3\" class=\"data row6 col3\" >13500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow6_col4\" class=\"data row6 col4\" >4466</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow6_col5\" class=\"data row6 col5\" >37500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow6_col6\" class=\"data row6 col6\" >2504</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow6_col7\" class=\"data row6 col7\" >0.64%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow7_col1\" class=\"data row7 col1\" >ruby</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow7_col2\" class=\"data row7 col2\" >15832</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow7_col3\" class=\"data row7 col3\" >14000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow7_col4\" class=\"data row7 col4\" >2500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow7_col5\" class=\"data row7 col5\" >31000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow7_col6\" class=\"data row7 col6\" >1260</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.32%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow8_col1\" class=\"data row8 col1\" >kotlin</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow8_col2\" class=\"data row8 col2\" >15414</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow8_col3\" class=\"data row8 col3\" >14000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow8_col4\" class=\"data row8 col4\" >6500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow8_col5\" class=\"data row8 col5\" >30000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow8_col6\" class=\"data row8 col6\" >1209</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.31%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow9_col1\" class=\"data row9 col1\" >cpp</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow9_col2\" class=\"data row9 col2\" >15393</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow9_col3\" class=\"data row9 col3\" >12958</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow9_col4\" class=\"data row9 col4\" >3750</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow9_col5\" class=\"data row9 col5\" >37500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow9_col6\" class=\"data row9 col6\" >63689</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow9_col7\" class=\"data row9 col7\" >16.38%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow10_col1\" class=\"data row10 col1\" >julia</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow10_col2\" class=\"data row10 col2\" >15167</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow10_col3\" class=\"data row10 col3\" >15167</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow10_col4\" class=\"data row10 col4\" >2500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow10_col5\" class=\"data row10 col5\" >21500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow10_col6\" class=\"data row10 col6\" >3</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow10_col7\" class=\"data row10 col7\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow11_col1\" class=\"data row11 col1\" >swift</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow11_col2\" class=\"data row11 col2\" >14722</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow11_col3\" class=\"data row11 col3\" >12500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow11_col4\" class=\"data row11 col4\" >5250</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow11_col5\" class=\"data row11 col5\" >30000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow11_col6\" class=\"data row11 col6\" >3204</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.82%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow12_col1\" class=\"data row12 col1\" >typescript</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow12_col2\" class=\"data row12 col2\" >14111</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow12_col3\" class=\"data row12 col3\" >12500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow12_col4\" class=\"data row12 col4\" >6108</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow12_col5\" class=\"data row12 col5\" >30661</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow12_col6\" class=\"data row12 col6\" >863</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.22%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow13_col1\" class=\"data row13 col1\" >objective_c</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow13_col2\" class=\"data row13 col2\" >13685</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow13_col3\" class=\"data row13 col3\" >12500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow13_col4\" class=\"data row13 col4\" >5250</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow13_col5\" class=\"data row13 col5\" >22500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow13_col6\" class=\"data row13 col6\" >304</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow13_col7\" class=\"data row13 col7\" >0.08%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow14_col1\" class=\"data row14 col1\" >java</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow14_col2\" class=\"data row14 col2\" >13259</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow14_col3\" class=\"data row14 col3\" >12500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow14_col4\" class=\"data row14 col4\" >3750</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow14_col5\" class=\"data row14 col5\" >30000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow14_col6\" class=\"data row14 col6\" >130436</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow14_col7\" class=\"data row14 col7\" >33.54%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow15_col1\" class=\"data row15 col1\" >php</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow15_col2\" class=\"data row15 col2\" >12836</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow15_col3\" class=\"data row15 col3\" >11500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow15_col4\" class=\"data row15 col4\" >3750</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow15_col5\" class=\"data row15 col5\" >32500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow15_col6\" class=\"data row15 col6\" >17586</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow15_col7\" class=\"data row15 col7\" >4.52%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow16_col1\" class=\"data row16 col1\" >javascript</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow16_col2\" class=\"data row16 col2\" >11543</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow16_col3\" class=\"data row16 col3\" >10833</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow16_col4\" class=\"data row16 col4\" >3750</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow16_col5\" class=\"data row16 col5\" >24562</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow16_col6\" class=\"data row16 col6\" >48955</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow16_col7\" class=\"data row16 col7\" >12.59%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow17_col1\" class=\"data row17 col1\" >c_sharp</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow17_col2\" class=\"data row17 col2\" >11377</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow17_col3\" class=\"data row17 col3\" >10500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow17_col4\" class=\"data row17 col4\" >3750</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow17_col5\" class=\"data row17 col5\" >25000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow17_col6\" class=\"data row17 col6\" >49727</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow17_col7\" class=\"data row17 col7\" >12.79%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow18_col1\" class=\"data row18 col1\" >visual_basic</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow18_col2\" class=\"data row18 col2\" >11094</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow18_col3\" class=\"data row18 col3\" >11500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow18_col4\" class=\"data row18 col4\" >4500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow18_col5\" class=\"data row18 col5\" >21787</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow18_col6\" class=\"data row18 col6\" >39</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow18_col7\" class=\"data row18 col7\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow19_col1\" class=\"data row19 col1\" >vba</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow19_col2\" class=\"data row19 col2\" >10924</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow19_col3\" class=\"data row19 col3\" >10500</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow19_col4\" class=\"data row19 col4\" >2530</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow19_col5\" class=\"data row19 col5\" >23200</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow19_col6\" class=\"data row19 col6\" >412</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow20_col1\" class=\"data row20 col1\" >delphi</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow20_col2\" class=\"data row20 col2\" >10673</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow20_col3\" class=\"data row20 col3\" >10000</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow20_col4\" class=\"data row20 col4\" >3750</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow20_col5\" class=\"data row20 col5\" >21250</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow20_col6\" class=\"data row20 col6\" >1245</td> \n",
       "        <td id=\"T_77d157dc_e58e_11e9_9929_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.32%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x20559ece438>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_77d43d00_e58e_11e9_bc25_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow0_col1\" class=\"data row0 col1\" >java</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow0_col2\" class=\"data row0 col2\" >33.54%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow1_col1\" class=\"data row1 col1\" >cpp</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow1_col2\" class=\"data row1 col2\" >16.38%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow2_col1\" class=\"data row2 col1\" >c_sharp</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow2_col2\" class=\"data row2 col2\" >12.79%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow3_col1\" class=\"data row3 col1\" >javascript</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow3_col2\" class=\"data row3 col2\" >12.59%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow4_col2\" class=\"data row4 col2\" >7.68%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow5_col1\" class=\"data row5 col1\" >go</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow5_col2\" class=\"data row5 col2\" >7.25%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow6_col1\" class=\"data row6 col1\" >php</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow6_col2\" class=\"data row6 col2\" >4.52%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow7_col1\" class=\"data row7 col1\" >matlab</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow7_col2\" class=\"data row7 col2\" >1.47%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow8_col1\" class=\"data row8 col1\" >lua</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow8_col2\" class=\"data row8 col2\" >0.83%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow9_col1\" class=\"data row9 col1\" >swift</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow9_col2\" class=\"data row9 col2\" >0.82%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow10_col1\" class=\"data row10 col1\" >perl</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow10_col2\" class=\"data row10 col2\" >0.64%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow11_col1\" class=\"data row11 col1\" >ruby</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow11_col2\" class=\"data row11 col2\" >0.32%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow12_col1\" class=\"data row12 col1\" >delphi</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow12_col2\" class=\"data row12 col2\" >0.32%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow13_col1\" class=\"data row13 col1\" >kotlin</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow13_col2\" class=\"data row13 col2\" >0.31%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow14_col1\" class=\"data row14 col1\" >typescript</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow14_col2\" class=\"data row14 col2\" >0.22%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow15_col1\" class=\"data row15 col1\" >vba</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow15_col2\" class=\"data row15 col2\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow16_col1\" class=\"data row16 col1\" >rust</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow16_col2\" class=\"data row16 col2\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow17_col1\" class=\"data row17 col1\" >objective_c</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow17_col2\" class=\"data row17 col2\" >0.08%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow18_col1\" class=\"data row18 col1\" >haskell</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow18_col2\" class=\"data row18 col2\" >0.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow19_col1\" class=\"data row19 col1\" >visual_basic</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow19_col2\" class=\"data row19 col2\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow20_col1\" class=\"data row20 col1\" >julia</td> \n",
       "        <td id=\"T_77d43d00_e58e_11e9_bc25_701ce71031efrow20_col2\" class=\"data row20 col2\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x20547808828>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=data_pl[['pl_','percentage']].sort_values(by='percentage', ascending=False)\n",
    "data_pl['rank']=range(1,22)\n",
    "data_pl=data_pl[['rank','pl_','percentage']]\n",
    "#data_pl.style.format({\"percentage\":\"{:.2%}\"})\n",
    "data_pl.style.hide_index().format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pl=data_pl.sort_values(by='percentage')\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = data_pl['pl_']\n",
    "sizes = data_pl['percentage']\n",
    "#explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(15, 9))\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title(\"Programming Languages in China, March 2019\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "wc=WordCloud()\n",
    "wc_dict = {}\n",
    "for index, row in data_pl.iterrows():\n",
    "    wc_dict[row['pl_']]=row['percentage']\n",
    "\n",
    "\n",
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc_dict['c/c++']=0.33\n",
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
